问题
Actually,My problem is based on the :
Is there a faster way to update dataframe column values based on conditions?
So,the data should be:
import pandas as pd
import io
t="""
AV4MdG6Ihowv-SKBN_nB DTP,FOOD
AV4Mc2vNhowv-SKBN_Rn Cash 1,FOOD
AV4MeisikOpWpLdepWy6 DTP,Bar
AV4MeRh6howv-SKBOBOn Cash 1,FOOD
AV4Mezwchowv-SKBOB_S DTOT,Bar
AV4MeB7yhowv-SKBOA5b DTP,Bar
"""
data_vec=pd.read_csv(io.StringIO(t),sep='\s{2,}',names=['id','source'])
data_vec
This is the data_vec:
id source
0 AV4MdG6Ihowv-SKBN_nB DTP,FOOD
1 AV4Mc2vNhowv-SKBN_Rn Cash 1,FOOD
2 AV4MeisikOpWpLdepWy6 DTP,Bar
3 AV4MeRh6howv-SKBOBOn Cash 1,FOOD
4 AV4Mezwchowv-SKBOB_S DTOT,Bar
5 AV4MeB7yhowv-SKBOA5b DTP,Bar
If I want the result like follow:(It means how to vectorize the mutipletags or categories ?)
_id source_Cash 1 source_DTOT source_DTP Food Bar
0 AV4MdG6Ihowv-SKBN_nB 0 0 1 1 0
1 AV4Mc2vNhowv-SKBN_Rn 1 0 0 1 0
2 AV4MeisikOpWpLdepWy6 0 0 1 0 1
3 AV4MeRh6howv-SKBOBOn 1 0 0 1 0
4 AV4Mezwchowv-SKBOB_S 0 1 0 0 1
5 AV4MeB7yhowv-SKBOA5b 0 0 1 0 1
If it is duplicate, warn me to delete!
回答1:
A bit of str.split and pd.get_dummies magic, inspired by Scott Boston and improved (from original version) thanks to JohnE.
df = df.set_index('id').source.str.get_dummies(',')
df.columns = df.columns.str.split().str[0].str.lower()
df = df.add_prefix('source_').reset_index()
print(df)
id source_bar source_cash source_dtot source_dtp \
0 AV4MdG6Ihowv-SKBN_nB 0 0 0 1
1 AV4Mc2vNhowv-SKBN_Rn 0 1 0 0
2 AV4MeisikOpWpLdepWy6 1 0 0 1
3 AV4MeRh6howv-SKBOBOn 0 1 0 0
4 AV4Mezwchowv-SKBOB_S 1 0 1 0
5 AV4MeB7yhowv-SKBOA5b 1 0 0 1
source_food
0 1
1 1
2 0
3 1
4 0
5 0
回答2:
You could also do this: What I am doing is splitting the "Source" column and creating new rows.Then I call get_dummies on the source column, then groupby the "id" column.
data_vec = pd.DataFrame(pd.concat([pd.Series(row['id'], row['source'].split(','))
for _, row in data_vec.iterrows()])).reset_index()
data_vec.columns = ['source','id']
which gives:
source id
0 DTP AV4MdG6Ihowv-SKBN_nB
1 FOOD AV4MdG6Ihowv-SKBN_nB
2 Cash 1 AV4Mc2vNhowv-SKBN_Rn
3 FOOD AV4Mc2vNhowv-SKBN_Rn
4 DTP AV4MeisikOpWpLdepWy6
5 Bar AV4MeisikOpWpLdepWy6
6 Cash 1 AV4MeRh6howv-SKBOBOn
7 FOOD AV4MeRh6howv-SKBOBOn
8 DTOT AV4Mezwchowv-SKBOB_S
9 Bar AV4Mezwchowv-SKBOB_S
10 DTP AV4MeB7yhowv-SKBOA5b
11 Bar AV4MeB7yhowv-SKBOA5b
then call get_dummies() on the source column:
result = pd.concat([data_vec.get(['id']),
pd.get_dummies(data_vec['source'], prefix='source')],axis=1)
result.groupby('id').sum().reset_index()
Which gives:
id source_Bar source_Cash 1 source_DTOT source_DTP source_FOOD
0 AV4Mc2vNhowv-SKBN_Rn 0 1 0 0 1
1 AV4MdG6Ihowv-SKBN_nB 0 0 0 1 1
2 AV4MeB7yhowv-SKBOA5b 1 0 0 1 0
3 AV4MeRh6howv-SKBOBOn 0 1 0 0 1
4 AV4MeisikOpWpLdepWy6 1 0 0 1 0
5 AV4Mezwchowv-SKBOB_S 1 0 1 0 0
来源：https://stackoverflow.com/questions/46679401/how-to-do-pd-get-dummies-or-other-ways